Overview

Dataset statistics

Number of variables38
Number of observations194673
Missing cells1100024
Missing cells (%)14.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory286.9 MiB
Average record size in memory1.5 KiB

Variable types

CAT23
NUM13
BOOL1
UNSUPPORTED1

Reproduction

Analysis started2020-09-02 12:04:43.652487
Analysis finished2020-09-02 12:05:38.703320
Duration55.05 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

REPORTNO has a high cardinality: 194670 distinct values High cardinality
LOCATION has a high cardinality: 24102 distinct values High cardinality
INCDATE has a high cardinality: 5985 distinct values High cardinality
INCDTTM has a high cardinality: 162058 distinct values High cardinality
ST_COLDESC has a high cardinality: 62 distinct values High cardinality
INCKEY is highly correlated with OBJECTID and 2 other fieldsHigh correlation
OBJECTID is highly correlated with INCKEY and 2 other fieldsHigh correlation
COLDETKEY is highly correlated with OBJECTID and 2 other fieldsHigh correlation
SEVERITYCODE.1 is highly correlated with SEVERITYCODEHigh correlation
SEVERITYCODE is highly correlated with SEVERITYCODE.1High correlation
SDOTCOLNUM is highly correlated with OBJECTID and 2 other fieldsHigh correlation
SEVERITYCODE.1 is highly correlated with SEVERITYCODE and 1 other fieldsHigh correlation
SEVERITYCODE is highly correlated with SEVERITYCODE.1 and 1 other fieldsHigh correlation
SEVERITYDESC is highly correlated with SEVERITYCODE and 1 other fieldsHigh correlation
ST_COLDESC is highly correlated with COLLISIONTYPEHigh correlation
COLLISIONTYPE is highly correlated with ST_COLDESCHigh correlation
X has 5334 (2.7%) missing values Missing
Y has 5334 (2.7%) missing values Missing
INTKEY has 129603 (66.6%) missing values Missing
LOCATION has 2677 (1.4%) missing values Missing
EXCEPTRSNCODE has 109862 (56.4%) missing values Missing
EXCEPTRSNDESC has 189035 (97.1%) missing values Missing
COLLISIONTYPE has 4904 (2.5%) missing values Missing
JUNCTIONTYPE has 6329 (3.3%) missing values Missing
INATTENTIONIND has 164868 (84.7%) missing values Missing
UNDERINFL has 4884 (2.5%) missing values Missing
WEATHER has 5081 (2.6%) missing values Missing
ROADCOND has 5012 (2.6%) missing values Missing
LIGHTCOND has 5170 (2.7%) missing values Missing
PEDROWNOTGRNT has 190006 (97.6%) missing values Missing
SDOTCOLNUM has 79737 (41.0%) missing values Missing
SPEEDING has 185340 (95.2%) missing values Missing
ST_COLDESC has 4904 (2.5%) missing values Missing
SEGLANEKEY is highly skewed (γ1 = 66.46373104) Skewed
REPORTNO is uniformly distributed Uniform
OBJECTID has unique values Unique
INCKEY has unique values Unique
COLDETKEY has unique values Unique
ST_COLCODE is an unsupported type, check if it needs cleaning or further analysis Unsupported
PERSONCOUNT has 5544 (2.8%) zeros Zeros
PEDCOUNT has 187734 (96.4%) zeros Zeros
VEHCOUNT has 5085 (2.6%) zeros Zeros
SDOT_COLCODE has 9787 (5.0%) zeros Zeros
SEGLANEKEY has 191907 (98.6%) zeros Zeros
CROSSWALKKEY has 190862 (98.0%) zeros Zeros

Variables

SEVERITYCODE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
136485
2
58188
ValueCountFrequency (%) 
113648570.1%
 
25818829.9%
 
2020-09-02T20:05:39.402788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
113648570.1%
 
25818829.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number194673100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
113648570.1%
 
25818829.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common194673100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
113648570.1%
 
25818829.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII194673100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
113648570.1%
 
25818829.9%
 

X
Real number (ℝ)

MISSING

Distinct count23563
Unique (%)12.4%
Missing5334
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean-122.33051843903844
Minimum-122.41909109999999
Maximum-122.2389494
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-09-02T20:05:39.486767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-122.4190911
5-th percentile-122.382899
Q1-122.3486733
median-122.3302243
Q3-122.3119374
95-th percentile-122.2798291
Maximum-122.2389494
Range0.1801417
Interquartile range (IQR)0.0367359

Descriptive statistics

Standard deviation0.02997605241
Coefficient of variation (CV)-0.0002450414891
Kurtosis-0.2462084477
Mean-122.3305184
Median Absolute Deviation (MAD)0.0183386
Skewness-0.05886785187
Sum-23161938.03
Variance0.0008985637179
2020-09-02T20:05:39.567997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-122.33265332650.1%
 
-122.34489612540.1%
 
-122.32807862520.1%
 
-122.34499682390.1%
 
-122.29915972310.1%
 
-122.35113392120.1%
 
-122.34729431900.1%
 
-122.34586311630.1%
 
-122.33245131600.1%
 
-122.26998791520.1%
 
-122.32904871470.1%
 
-122.31094941460.1%
 
-122.28992291420.1%
 
-122.33466561380.1%
 
-122.32192041360.1%
 
-122.33917361360.1%
 
-122.3299741350.1%
 
-122.33557131330.1%
 
-122.3023291320.1%
 
-122.32461521310.1%
 
-122.2699821300.1%
 
-122.33943911290.1%
 
-122.33955941290.1%
 
-122.33375681280.1%
 
-122.31673341280.1%
 
Other values (23538)18520195.1%
 
(Missing)53342.7%
 
ValueCountFrequency (%) 
-122.41909111< 0.1%
 
-122.419031814< 0.1%
 
-122.41897251< 0.1%
 
-122.41875741< 0.1%
 
-122.41861538< 0.1%
 
-122.41813951< 0.1%
 
-122.4181212< 0.1%
 
-122.41711381< 0.1%
 
-122.41711298< 0.1%
 
-122.41705481< 0.1%
 
ValueCountFrequency (%) 
-122.238949439< 0.1%
 
-122.23978061< 0.1%
 
-122.24108214< 0.1%
 
-122.24108843< 0.1%
 
-122.24112072< 0.1%
 
-122.24114511< 0.1%
 
-122.24139235< 0.1%
 
-122.24140214< 0.1%
 
-122.24141425< 0.1%
 
-122.24199544< 0.1%
 

Y
Real number (ℝ≥0)

MISSING

Distinct count23839
Unique (%)12.6%
Missing5334
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean47.61954251768817
Minimum47.49557292
Maximum47.73414158
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-09-02T20:05:39.656763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum47.49557292
5-th percentile47.52704457
Q147.57595611
median47.61536892
Q347.66366435
95-th percentile47.71501837
Maximum47.73414158
Range0.23856866
Interquartile range (IQR)0.08770824

Descriptive statistics

Standard deviation0.05615663741
Coefficient of variation (CV)0.00117927713
Kurtosis-0.8169242526
Mean47.61954252
Median Absolute Deviation (MAD)0.04501764
Skewness0.06155334893
Sum9016236.561
Variance0.003153567925
2020-09-02T20:05:39.727987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
47.70865452650.1%
 
47.71717312540.1%
 
47.604161232520.1%
 
47.725035552390.1%
 
47.579673462310.1%
 
47.570941782120.1%
 
47.647172491900.1%
 
47.612990811610.1%
 
47.607266311600.1%
 
47.522815641520.1%
 
47.595116281460.1%
 
47.568882381420.1%
 
47.609685421380.1%
 
47.708585791360.1%
 
47.613727071360.1%
 
47.521783111330.1%
 
47.654995231320.1%
 
47.70860281310.1%
 
47.524739041300.1%
 
47.608324561290.1%
 
47.612889241280.1%
 
47.551176021280.1%
 
47.60869261280.1%
 
47.54702451260.1%
 
47.549190591250.1%
 
Other values (23814)18523595.2%
 
(Missing)53342.7%
 
ValueCountFrequency (%) 
47.495572921< 0.1%
 
47.495806672< 0.1%
 
47.495892661< 0.1%
 
47.4959893710< 0.1%
 
47.496251116< 0.1%
 
47.496402958< 0.1%
 
47.496485712< 0.1%
 
47.496503614< 0.1%
 
47.496512852< 0.1%
 
47.496664791< 0.1%
 
ValueCountFrequency (%) 
47.734141585< 0.1%
 
47.734140592< 0.1%
 
47.734138912< 0.1%
 
47.734136563< 0.1%
 
47.7341361325< 0.1%
 
47.734135763< 0.1%
 
47.734135554< 0.1%
 
47.734135341< 0.1%
 
47.734134967< 0.1%
 
47.7341345811< 0.1%
 

OBJECTID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count194673
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108479.3649299081
Minimum1
Maximum219547
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-09-02T20:05:39.857392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12238.6
Q154267
median106912
Q3162272
95-th percentile208009.4
Maximum219547
Range219546
Interquartile range (IQR)108005

Descriptive statistics

Standard deviation62649.72256
Coefficient of variation (CV)0.5775266347
Kurtosis-1.19041273
Mean108479.3649
Median Absolute Deviation (MAD)53944
Skewness0.04672710251
Sum2.111800341e+10
Variance3924987737
2020-09-02T20:05:39.927902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
11941< 0.1%
 
585501< 0.1%
 
646931< 0.1%
 
626441< 0.1%
 
524031< 0.1%
 
503541< 0.1%
 
564971< 0.1%
 
544481< 0.1%
 
155331< 0.1%
 
134841< 0.1%
 
73371< 0.1%
 
380721< 0.1%
 
52881< 0.1%
 
278151< 0.1%
 
257661< 0.1%
 
319091< 0.1%
 
298601< 0.1%
 
175701< 0.1%
 
237131< 0.1%
 
216641< 0.1%
 
1097271< 0.1%
 
605991< 0.1%
 
401211< 0.1%
 
1138211< 0.1%
 
Other values (194648)194648> 99.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
61< 0.1%
 
71< 0.1%
 
91< 0.1%
 
101< 0.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
2195471< 0.1%
 
2195461< 0.1%
 
2195451< 0.1%
 
2195441< 0.1%
 
2195431< 0.1%
 
2195411< 0.1%
 
2195391< 0.1%
 
2195381< 0.1%
 
2195371< 0.1%
 
2195361< 0.1%
 

INCKEY
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count194673
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141091.45634987904
Minimum1001
Maximum331454
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-09-02T20:05:40.055381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile28830.6
Q170383
median123363
Q3203319
95-th percentile317465.4
Maximum331454
Range330453
Interquartile range (IQR)132936

Descriptive statistics

Standard deviation86634.40274
Coefficient of variation (CV)0.6140301119
Kurtosis-0.6345565011
Mean141091.4563
Median Absolute Deviation (MAD)61515
Skewness0.6045684471
Sum2.746669708e+10
Variance7505519738
2020-09-02T20:05:40.131928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2662381< 0.1%
 
815491< 0.1%
 
1040881< 0.1%
 
1266151< 0.1%
 
1245661< 0.1%
 
1307091< 0.1%
 
1286601< 0.1%
 
1184191< 0.1%
 
1163701< 0.1%
 
1204641< 0.1%
 
754061< 0.1%
 
795001< 0.1%
 
999941< 0.1%
 
692591< 0.1%
 
672101< 0.1%
 
733531< 0.1%
 
713041< 0.1%
 
3253501< 0.1%
 
917821< 0.1%
 
979251< 0.1%
 
958761< 0.1%
 
856351< 0.1%
 
1061371< 0.1%
 
1122841< 0.1%
 
876801< 0.1%
 
Other values (194648)194648> 99.9%
 
ValueCountFrequency (%) 
10011< 0.1%
 
10021< 0.1%
 
10031< 0.1%
 
10041< 0.1%
 
10051< 0.1%
 
10091< 0.1%
 
10111< 0.1%
 
10121< 0.1%
 
10131< 0.1%
 
10211< 0.1%
 
ValueCountFrequency (%) 
3314541< 0.1%
 
3314531< 0.1%
 
3314521< 0.1%
 
3314491< 0.1%
 
3314481< 0.1%
 
3314471< 0.1%
 
3314461< 0.1%
 
3314441< 0.1%
 
3314421< 0.1%
 
3314411< 0.1%
 

COLDETKEY
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count194673
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141298.81138113656
Minimum1001
Maximum332954
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-09-02T20:05:40.261349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile28830.6
Q170383
median123363
Q3203459
95-th percentile318965.4
Maximum332954
Range331953
Interquartile range (IQR)133076

Descriptive statistics

Standard deviation86986.54211
Coefficient of variation (CV)0.6156211879
Kurtosis-0.6217436719
Mean141298.8114
Median Absolute Deviation (MAD)61543
Skewness0.6123297057
Sum2.750706351e+10
Variance7566658508
2020-09-02T20:05:40.337769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2662381< 0.1%
 
1221291< 0.1%
 
1119001< 0.1%
 
1016591< 0.1%
 
996101< 0.1%
 
1057531< 0.1%
 
1037041< 0.1%
 
1262311< 0.1%
 
1241821< 0.1%
 
1303251< 0.1%
 
1282761< 0.1%
 
1200801< 0.1%
 
1078061< 0.1%
 
770711< 0.1%
 
750221< 0.1%
 
811651< 0.1%
 
791161< 0.1%
 
688751< 0.1%
 
668261< 0.1%
 
729691< 0.1%
 
3031611< 0.1%
 
934471< 0.1%
 
1139491< 0.1%
 
1098551< 0.1%
 
975411< 0.1%
 
Other values (194648)194648> 99.9%
 
ValueCountFrequency (%) 
10011< 0.1%
 
10021< 0.1%
 
10031< 0.1%
 
10041< 0.1%
 
10051< 0.1%
 
10091< 0.1%
 
10111< 0.1%
 
10121< 0.1%
 
10131< 0.1%
 
10211< 0.1%
 
ValueCountFrequency (%) 
3329541< 0.1%
 
3329531< 0.1%
 
3329521< 0.1%
 
3329491< 0.1%
 
3329481< 0.1%
 
3329471< 0.1%
 
3329461< 0.1%
 
3329441< 0.1%
 
3329421< 0.1%
 
3329411< 0.1%
 

REPORTNO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct count194670
Unique (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1782439
 
2
1776526
 
2
1780512
 
2
3285116
 
1
1776785
 
1
Other values (194665)
194665
ValueCountFrequency (%) 
17824392< 0.1%
 
17765262< 0.1%
 
17805122< 0.1%
 
32851161< 0.1%
 
17767851< 0.1%
 
E8928131< 0.1%
 
26192881< 0.1%
 
E4514901< 0.1%
 
35606311< 0.1%
 
35015361< 0.1%
 
26139621< 0.1%
 
37320121< 0.1%
 
35629211< 0.1%
 
26116951< 0.1%
 
33426251< 0.1%
 
E5954211< 0.1%
 
29046371< 0.1%
 
26251051< 0.1%
 
37425631< 0.1%
 
26035661< 0.1%
 
E6472941< 0.1%
 
C7206311< 0.1%
 
36438461< 0.1%
 
35518771< 0.1%
 
35822001< 0.1%
 
Other values (194645)194645> 99.9%
 
2020-09-02T20:05:41.381021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.998797984
Min length4

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
320441015.0%
 
215537211.4%
 
715038511.0%
 
61265059.3%
 
81243979.1%
 
51234399.1%
 
11180978.7%
 
01131438.3%
 
91103548.1%
 
41003487.4%
 
E266222.0%
 
C78290.6%
 
A15580.1%
 
_12< 0.1%
 
e3< 0.1%
 
c1< 0.1%
 
R1< 0.1%
 
S1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number132645097.4%
 
Uppercase Letter360112.6%
 
Connector Punctuation12< 0.1%
 
Lowercase Letter4< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
320441015.4%
 
215537211.7%
 
715038511.3%
 
61265059.5%
 
81243979.4%
 
51234399.3%
 
11180978.9%
 
01131438.5%
 
91103548.3%
 
41003487.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E2662273.9%
 
C782921.7%
 
A15584.3%
 
R1< 0.1%
 
S1< 0.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_12100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e375.0%
 
c125.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common132646297.4%
 
Latin360152.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
320441015.4%
 
215537211.7%
 
715038511.3%
 
61265059.5%
 
81243979.4%
 
51234399.3%
 
11180978.9%
 
01131438.5%
 
91103548.3%
 
41003487.6%
 
_12< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E2662273.9%
 
C782921.7%
 
A15584.3%
 
e3< 0.1%
 
c1< 0.1%
 
R1< 0.1%
 
S1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1362477100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
320441015.0%
 
215537211.4%
 
715038511.0%
 
61265059.3%
 
81243979.1%
 
51234399.1%
 
11180978.7%
 
01131438.3%
 
91103548.1%
 
41003487.4%
 
E266222.0%
 
C78290.6%
 
A15580.1%
 
_12< 0.1%
 
e3< 0.1%
 
c1< 0.1%
 
R1< 0.1%
 
S1< 0.1%
 

STATUS
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Matched
189786
Unmatched
 
4887
ValueCountFrequency (%) 
Matched18978697.5%
 
Unmatched48872.5%
 
2020-09-02T20:05:42.096965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.050207271
Min length7

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a19467314.2%
 
t19467314.2%
 
c19467314.2%
 
h19467314.2%
 
e19467314.2%
 
d19467314.2%
 
M18978613.8%
 
U48870.4%
 
n48870.4%
 
m48870.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter117781285.8%
 
Uppercase Letter19467314.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M18978697.5%
 
U48872.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a19467316.5%
 
t19467316.5%
 
c19467316.5%
 
h19467316.5%
 
e19467316.5%
 
d19467316.5%
 
n48870.4%
 
m48870.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1372485100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a19467314.2%
 
t19467314.2%
 
c19467314.2%
 
h19467314.2%
 
e19467314.2%
 
d19467314.2%
 
M18978613.8%
 
U48870.4%
 
n48870.4%
 
m48870.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1372485100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a19467314.2%
 
t19467314.2%
 
c19467314.2%
 
h19467314.2%
 
e19467314.2%
 
d19467314.2%
 
M18978613.8%
 
U48870.4%
 
n48870.4%
 
m48870.4%
 

ADDRTYPE
Categorical

Distinct count3
Unique (%)< 0.1%
Missing1926
Missing (%)1.0%
Memory size1.5 MiB
Block
126926
Intersection
65070
Alley
 
751
ValueCountFrequency (%) 
Block12692665.2%
 
Intersection6507033.4%
 
Alley7510.4%
 
(Missing)19261.0%
 
2020-09-02T20:05:42.818938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length12
Median length5
Mean length7.31998274
Min length3

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
c19199613.5%
 
o19199613.5%
 
n1339929.4%
 
e1308919.2%
 
t1301409.1%
 
l1284289.0%
 
B1269268.9%
 
k1269268.9%
 
I650704.6%
 
r650704.6%
 
s650704.6%
 
i650704.6%
 
a19260.1%
 
A7510.1%
 
y7510.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter123225686.5%
 
Uppercase Letter19274713.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B12692665.9%
 
I6507033.8%
 
A7510.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
c19199615.6%
 
o19199615.6%
 
n13399210.9%
 
e13089110.6%
 
t13014010.6%
 
l12842810.4%
 
k12692610.3%
 
r650705.3%
 
s650705.3%
 
i650705.3%
 
a19260.2%
 
y7510.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1425003100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
c19199613.5%
 
o19199613.5%
 
n1339929.4%
 
e1308919.2%
 
t1301409.1%
 
l1284289.0%
 
B1269268.9%
 
k1269268.9%
 
I650704.6%
 
r650704.6%
 
s650704.6%
 
i650704.6%
 
a19260.1%
 
A7510.1%
 
y7510.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1425003100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
c19199613.5%
 
o19199613.5%
 
n1339929.4%
 
e1308919.2%
 
t1301409.1%
 
l1284289.0%
 
B1269268.9%
 
k1269268.9%
 
I650704.6%
 
r650704.6%
 
s650704.6%
 
i650704.6%
 
a19260.1%
 
A7510.1%
 
y7510.1%
 

INTKEY
Real number (ℝ≥0)

MISSING

Distinct count7614
Unique (%)11.7%
Missing129603
Missing (%)66.6%
Infinite0
Infinite (%)0.0%
Mean37558.45057630244
Minimum23807.0
Maximum757580.0
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-09-02T20:05:42.892708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum23807
5-th percentile24509
Q128667
median29973
Q333973
95-th percentile37438
Maximum757580
Range733773
Interquartile range (IQR)5306

Descriptive statistics

Standard deviation51745.99027
Coefficient of variation (CV)1.377745607
Kurtosis71.75026612
Mean37558.45058
Median Absolute Deviation (MAD)2849
Skewness8.289057666
Sum2443928379
Variance2677647509
2020-09-02T20:05:42.961417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
299732520.1%
 
299331600.1%
 
299131380.1%
 
295491360.1%
 
297611280.1%
 
299301280.1%
 
335121280.1%
 
295761170.1%
 
298781170.1%
 
290521150.1%
 
293801150.1%
 
296221130.1%
 
364191060.1%
 
299291040.1%
 
299631030.1%
 
304101010.1%
 
331351010.1%
 
304821000.1%
 
29515990.1%
 
2986597< 0.1%
 
2961596< 0.1%
 
2876096< 0.1%
 
2991495< 0.1%
 
3050994< 0.1%
 
3582792< 0.1%
 
Other values (7589)6213931.9%
 
(Missing)12960366.6%
 
ValueCountFrequency (%) 
238075< 0.1%
 
238082< 0.1%
 
238111< 0.1%
 
238141< 0.1%
 
238152< 0.1%
 
238331< 0.1%
 
238433< 0.1%
 
238555< 0.1%
 
2386052< 0.1%
 
238612< 0.1%
 
ValueCountFrequency (%) 
7575801< 0.1%
 
7254041< 0.1%
 
7198621< 0.1%
 
7018171< 0.1%
 
6923451< 0.1%
 
6739742< 0.1%
 
6734741< 0.1%
 
6734711< 0.1%
 
6623162< 0.1%
 
6416261< 0.1%
 

LOCATION
Categorical

HIGH CARDINALITY
MISSING

Distinct count24102
Unique (%)12.6%
Missing2677
Missing (%)1.4%
Memory size1.5 MiB
BATTERY ST TUNNEL NB BETWEEN ALASKAN WY VI NB AND AURORA AVE N
 
276
BATTERY ST TUNNEL SB BETWEEN AURORA AVE N AND ALASKAN WY VI SB
 
271
N NORTHGATE WAY BETWEEN MERIDIAN AVE N AND CORLISS AVE N
 
265
AURORA AVE N BETWEEN N 117TH PL AND N 125TH ST
 
254
6TH AVE AND JAMES ST
 
252
Other values (24097)
190678
ValueCountFrequency (%) 
BATTERY ST TUNNEL NB BETWEEN ALASKAN WY VI NB AND AURORA AVE N2760.1%
 
BATTERY ST TUNNEL SB BETWEEN AURORA AVE N AND ALASKAN WY VI SB2710.1%
 
N NORTHGATE WAY BETWEEN MERIDIAN AVE N AND CORLISS AVE N2650.1%
 
AURORA AVE N BETWEEN N 117TH PL AND N 125TH ST2540.1%
 
6TH AVE AND JAMES ST2520.1%
 
AURORA AVE N BETWEEN N 130TH ST AND N 135TH ST2390.1%
 
ALASKAN WY VI NB BETWEEN S ROYAL BROUGHAM WAY ON RP AND SENECA ST OFF RP2380.1%
 
RAINIER AVE S BETWEEN S BAYVIEW ST AND S MCCLELLAN ST2310.1%
 
ALASKAN WY VI SB BETWEEN COLUMBIA ST ON RP AND ALASKAN WY VI SB EFR OFF RP2120.1%
 
WEST SEATTLE BR EB BETWEEN ALASKAN WY VI NB ON RP AND DELRIDGE-W SEATTLE BR EB ON RP2120.1%
 
AURORA BR BETWEEN RAYE ST AND BRIDGE WAY N1900.1%
 
ALASKAN WY VI NB BETWEEN SENECA ST OFF RP AND WESTERN AV OFF RP1640.1%
 
1ST AVE BETWEEN BLANCHARD ST AND BELL ST1610.1%
 
5TH AVE AND SPRING ST1600.1%
 
RAINIER AVE S BETWEEN S HENDERSON ST AND S DIRECTOR N ST1520.1%
 
RAINIER AVE S BETWEEN S DEARBORN ST AND S CHARLES N ST1460.1%
 
RAINIER AVE S BETWEEN S CHARLESTOWN ST AND S ANDOVER ST1420.1%
 
5TH AVE AND UNION ST1380.1%
 
5TH AVE AND VIRGINIA ST1360.1%
 
NE NORTHGATE WAY BETWEEN 5TH AVE NE AND 8TH AVE NE1360.1%
 
OLSON PL SW BETWEEN 1ST AVE S AND 2ND AVE SW1330.1%
 
MONTLAKE BLVD NE BETWEEN NE PACIFIC PL AND 25TH AVE NE1320.1%
 
NE NORTHGATE WAY BETWEEN 3RD AVE NE AND 5TH AVE NE1310.1%
 
RAINIER AVE S BETWEEN S CLOVERDALE ST AND S HENDERSON ST1300.1%
 
1ST AVE BETWEEN UNION ST AND PIKE ST1290.1%
 
Other values (24077)18736696.2%
 
(Missing)26771.4%
 
2020-09-02T20:05:43.709334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length90
Median length45
Mean length40.99166808
Min length3

Overview of Unicode Properties

Unique unicode characters40
Unique unicode categories (?)5
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
164893720.7%
 
E95254911.9%
 
N7251309.1%
 
A6829468.6%
 
T6265747.9%
 
S5219016.5%
 
W3189364.0%
 
D3092913.9%
 
R2524283.2%
 
V2429003.0%
 
H2032542.5%
 
B1880232.4%
 
O1710252.1%
 
L1588342.0%
 
I1401491.8%
 
Y957561.2%
 
1772901.0%
 
M632360.8%
 
5569470.7%
 
C549180.7%
 
P486380.6%
 
K483220.6%
 
G478230.6%
 
2455060.6%
 
4435660.5%
 
Other values (15)2550923.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter594772674.5%
 
Space Separator164893720.7%
 
Decimal Number3741014.7%
 
Lowercase Letter80310.1%
 
Dash Punctuation1176< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
17729020.7%
 
55694715.2%
 
24550612.2%
 
44356611.6%
 
34004510.7%
 
0263447.0%
 
6242696.5%
 
7227166.1%
 
8218555.8%
 
9155634.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E95254916.0%
 
N72513012.2%
 
A68294611.5%
 
T62657410.5%
 
S5219018.8%
 
W3189365.4%
 
D3092915.2%
 
R2524284.2%
 
V2429004.1%
 
H2032543.4%
 
B1880233.2%
 
O1710252.9%
 
L1588342.7%
 
I1401492.4%
 
Y957561.6%
 
M632361.1%
 
C549180.9%
 
P486380.8%
 
K483220.8%
 
G478230.8%
 
U418440.7%
 
F326520.5%
 
J148430.2%
 
X32440.1%
 
Q2052< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1648937100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n535466.7%
 
a267733.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1176100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin595575774.6%
 
Common202421425.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
164893781.5%
 
1772903.8%
 
5569472.8%
 
2455062.2%
 
4435662.2%
 
3400452.0%
 
0263441.3%
 
6242691.2%
 
7227161.1%
 
8218551.1%
 
9155630.8%
 
-11760.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E95254916.0%
 
N72513012.2%
 
A68294611.5%
 
T62657410.5%
 
S5219018.8%
 
W3189365.4%
 
D3092915.2%
 
R2524284.2%
 
V2429004.1%
 
H2032543.4%
 
B1880233.2%
 
O1710252.9%
 
L1588342.7%
 
I1401492.4%
 
Y957561.6%
 
M632361.1%
 
C549180.9%
 
P486380.8%
 
K483220.8%
 
G478230.8%
 
U418440.7%
 
F326520.5%
 
J148430.2%
 
n53540.1%
 
X32440.1%
 
Other values (3)51870.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7979971100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
164893720.7%
 
E95254911.9%
 
N7251309.1%
 
A6829468.6%
 
T6265747.9%
 
S5219016.5%
 
W3189364.0%
 
D3092913.9%
 
R2524283.2%
 
V2429003.0%
 
H2032542.5%
 
B1880232.4%
 
O1710252.1%
 
L1588342.0%
 
I1401491.8%
 
Y957561.2%
 
1772901.0%
 
M632360.8%
 
5569470.7%
 
C549180.7%
 
P486380.6%
 
K483220.6%
 
G478230.6%
 
2455060.6%
 
4435660.5%
 
Other values (15)2550923.2%
 

EXCEPTRSNCODE
Categorical

MISSING

Distinct count2
Unique (%)< 0.1%
Missing109862
Missing (%)56.4%
Memory size1.5 MiB
79173
NEI
 
5638
ValueCountFrequency (%) 
7917340.7%
 
NEI56382.9%
 
(Missing)10986256.4%
 
2020-09-02T20:05:44.422708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.18660523
Min length1

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories (?)3
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n21972451.6%
 
a10986225.8%
 
7917318.6%
 
N56381.3%
 
E56381.3%
 
I56381.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter32958677.4%
 
Space Separator7917318.6%
 
Uppercase Letter169144.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
79173100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n21972466.7%
 
a10986233.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N563833.3%
 
E563833.3%
 
I563833.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin34650081.4%
 
Common7917318.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
79173100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n21972463.4%
 
a10986231.7%
 
N56381.6%
 
E56381.6%
 
I56381.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII425673100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n21972451.6%
 
a10986225.8%
 
7917318.6%
 
N56381.3%
 
E56381.3%
 
I56381.3%
 

EXCEPTRSNDESC
Categorical

MISSING

Distinct count1
Unique (%)< 0.1%
Missing189035
Missing (%)97.1%
Memory size1.5 MiB
Not Enough Information, or Insufficient Location Information
5638
ValueCountFrequency (%) 
Not Enough Information, or Insufficient Location Information56382.9%
 
(Missing)18903597.1%
 
2020-09-02T20:05:45.139894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length60
Median length3
Mean length4.650799032
Min length3

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n42317446.7%
 
a20594922.7%
 
o507425.6%
 
338283.7%
 
t281903.1%
 
i281903.1%
 
f225522.5%
 
I169141.9%
 
r169141.9%
 
u112761.2%
 
m112761.2%
 
c112761.2%
 
N56380.6%
 
E56380.6%
 
g56380.6%
 
h56380.6%
 
,56380.6%
 
s56380.6%
 
e56380.6%
 
L56380.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter83209191.9%
 
Uppercase Letter338283.7%
 
Space Separator338283.7%
 
Other Punctuation56380.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n42317450.9%
 
a20594924.8%
 
o507426.1%
 
t281903.4%
 
i281903.4%
 
f225522.7%
 
r169142.0%
 
u112761.4%
 
m112761.4%
 
c112761.4%
 
g56380.7%
 
h56380.7%
 
s56380.7%
 
e56380.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I1691450.0%
 
N563816.7%
 
E563816.7%
 
L563816.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
33828100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,5638100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin86591995.6%
 
Common394664.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n42317448.9%
 
a20594923.8%
 
o507425.9%
 
t281903.3%
 
i281903.3%
 
f225522.6%
 
I169142.0%
 
r169142.0%
 
u112761.3%
 
m112761.3%
 
c112761.3%
 
N56380.7%
 
E56380.7%
 
g56380.7%
 
h56380.7%
 
s56380.7%
 
e56380.7%
 
L56380.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
3382885.7%
 
,563814.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII905385100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n42317446.7%
 
a20594922.7%
 
o507425.6%
 
338283.7%
 
t281903.1%
 
i281903.1%
 
f225522.5%
 
I169141.9%
 
r169141.9%
 
u112761.2%
 
m112761.2%
 
c112761.2%
 
N56380.6%
 
E56380.6%
 
g56380.6%
 
h56380.6%
 
,56380.6%
 
s56380.6%
 
e56380.6%
 
L56380.6%
 

SEVERITYCODE.1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
136485
2
58188
ValueCountFrequency (%) 
113648570.1%
 
25818829.9%
 
2020-09-02T20:05:45.851708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
113648570.1%
 
25818829.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number194673100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
113648570.1%
 
25818829.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common194673100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
113648570.1%
 
25818829.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII194673100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
113648570.1%
 
25818829.9%
 

SEVERITYDESC
Categorical

HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Property Damage Only Collision
136485
Injury Collision
58188
ValueCountFrequency (%) 
Property Damage Only Collision13648570.1%
 
Injury Collision5818829.9%
 
2020-09-02T20:05:46.577385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length30
Median length30
Mean length25.81538272
Min length16

Overview of Unicode Properties

Unique unicode characters21
Unique unicode categories (?)3
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o52583110.5%
 
l52583110.5%
 
4676439.3%
 
n3893467.7%
 
i3893467.7%
 
r3311586.6%
 
y3311586.6%
 
e2729705.4%
 
a2729705.4%
 
C1946733.9%
 
s1946733.9%
 
P1364852.7%
 
p1364852.7%
 
t1364852.7%
 
D1364852.7%
 
m1364852.7%
 
g1364852.7%
 
O1364852.7%
 
I581881.2%
 
j581881.2%
 
u581881.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter389559977.5%
 
Uppercase Letter66231613.2%
 
Space Separator4676439.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C19467329.4%
 
P13648520.6%
 
D13648520.6%
 
O13648520.6%
 
I581888.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o52583113.5%
 
l52583113.5%
 
n38934610.0%
 
i38934610.0%
 
r3311588.5%
 
y3311588.5%
 
e2729707.0%
 
a2729707.0%
 
s1946735.0%
 
p1364853.5%
 
t1364853.5%
 
m1364853.5%
 
g1364853.5%
 
j581881.5%
 
u581881.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
467643100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin455791590.7%
 
Common4676439.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o52583111.5%
 
l52583111.5%
 
n3893468.5%
 
i3893468.5%
 
r3311587.3%
 
y3311587.3%
 
e2729706.0%
 
a2729706.0%
 
C1946734.3%
 
s1946734.3%
 
P1364853.0%
 
p1364853.0%
 
t1364853.0%
 
D1364853.0%
 
m1364853.0%
 
g1364853.0%
 
O1364853.0%
 
I581881.3%
 
j581881.3%
 
u581881.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
467643100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5025558100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o52583110.5%
 
l52583110.5%
 
4676439.3%
 
n3893467.7%
 
i3893467.7%
 
r3311586.6%
 
y3311586.6%
 
e2729705.4%
 
a2729705.4%
 
C1946733.9%
 
s1946733.9%
 
P1364852.7%
 
p1364852.7%
 
t1364852.7%
 
D1364852.7%
 
m1364852.7%
 
g1364852.7%
 
O1364852.7%
 
I581881.2%
 
j581881.2%
 
u581881.2%
 

COLLISIONTYPE
Categorical

HIGH CORRELATION
MISSING

Distinct count10
Unique (%)< 0.1%
Missing4904
Missing (%)2.5%
Memory size1.5 MiB
Parked Car
47987
Angles
34674
Rear Ended
34090
Other
23703
Sideswipe
18609
Other values (5)
30706
ValueCountFrequency (%) 
Parked Car4798724.7%
 
Angles3467417.8%
 
Rear Ended3409017.5%
 
Other2370312.2%
 
Sideswipe186099.6%
 
Left Turn137037.0%
 
Pedestrian66083.4%
 
Cycles54152.8%
 
Right Turn29561.5%
 
Head On20241.0%
 
(Missing)49042.5%
 
2020-09-02T20:05:47.298742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.193981703
Min length3

Overview of Unicode Properties

Unique unicode characters29
Unique unicode categories (?)3
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e24612015.4%
 
r17703411.1%
 
a1436009.0%
 
d1434089.0%
 
n1038636.5%
 
1007606.3%
 
s653064.1%
 
P545953.4%
 
C534023.3%
 
k479873.0%
 
t469702.9%
 
i467822.9%
 
l400892.5%
 
g376302.4%
 
R370462.3%
 
A346742.2%
 
E340902.1%
 
h266591.7%
 
O257271.6%
 
S186091.2%
 
w186091.2%
 
p186091.2%
 
T166591.0%
 
u166591.0%
 
L137030.9%
 
Other values (4)265571.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter120385875.5%
 
Uppercase Letter29052918.2%
 
Space Separator1007606.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P5459518.8%
 
C5340218.4%
 
R3704612.8%
 
A3467411.9%
 
E3409011.7%
 
O257278.9%
 
S186096.4%
 
T166595.7%
 
L137034.7%
 
H20240.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e24612020.4%
 
r17703414.7%
 
a14360011.9%
 
d14340811.9%
 
n1038638.6%
 
s653065.4%
 
k479874.0%
 
t469703.9%
 
i467823.9%
 
l400893.3%
 
g376303.1%
 
h266592.2%
 
w186091.5%
 
p186091.5%
 
u166591.4%
 
f137031.1%
 
y54150.4%
 
c54150.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
100760100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin149438793.7%
 
Common1007606.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e24612016.5%
 
r17703411.8%
 
a1436009.6%
 
d1434089.6%
 
n1038637.0%
 
s653064.4%
 
P545953.7%
 
C534023.6%
 
k479873.2%
 
t469703.1%
 
i467823.1%
 
l400892.7%
 
g376302.5%
 
R370462.5%
 
A346742.3%
 
E340902.3%
 
h266591.8%
 
O257271.7%
 
S186091.2%
 
w186091.2%
 
p186091.2%
 
T166591.1%
 
u166591.1%
 
L137030.9%
 
f137030.9%
 
Other values (3)128540.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
100760100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1595147100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e24612015.4%
 
r17703411.1%
 
a1436009.0%
 
d1434089.0%
 
n1038636.5%
 
1007606.3%
 
s653064.1%
 
P545953.4%
 
C534023.3%
 
k479873.0%
 
t469702.9%
 
i467822.9%
 
l400892.5%
 
g376302.4%
 
R370462.3%
 
A346742.2%
 
E340902.1%
 
h266591.7%
 
O257271.6%
 
S186091.2%
 
w186091.2%
 
p186091.2%
 
T166591.0%
 
u166591.0%
 
L137030.9%
 
Other values (4)265571.7%
 

PERSONCOUNT
Real number (ℝ≥0)

ZEROS

Distinct count47
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.444427321713848
Minimum0
Maximum81
Zeros5544
Zeros (%)2.8%
Memory size1.5 MiB
2020-09-02T20:05:47.376883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum81
Range81
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.345928746
Coefficient of variation (CV)0.5506110712
Kurtosis201.9354891
Mean2.444427322
Median Absolute Deviation (MAD)0
Skewness7.26215714
Sum475864
Variance1.811524189
2020-09-02T20:05:47.458426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
211423158.7%
 
33555318.3%
 
4146607.5%
 
1131546.8%
 
565843.4%
 
055442.8%
 
627021.4%
 
711310.6%
 
85330.3%
 
92160.1%
 
101280.1%
 
1156< 0.1%
 
1233< 0.1%
 
1321< 0.1%
 
1419< 0.1%
 
1511< 0.1%
 
1711< 0.1%
 
168< 0.1%
 
446< 0.1%
 
186< 0.1%
 
206< 0.1%
 
256< 0.1%
 
195< 0.1%
 
264< 0.1%
 
224< 0.1%
 
Other values (22)41< 0.1%
 
ValueCountFrequency (%) 
055442.8%
 
1131546.8%
 
211423158.7%
 
33555318.3%
 
4146607.5%
 
565843.4%
 
627021.4%
 
711310.6%
 
85330.3%
 
92160.1%
 
ValueCountFrequency (%) 
811< 0.1%
 
571< 0.1%
 
541< 0.1%
 
531< 0.1%
 
481< 0.1%
 
473< 0.1%
 
446< 0.1%
 
431< 0.1%
 
411< 0.1%
 
391< 0.1%
 

PEDCOUNT
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.037139202662927064
Minimum0
Maximum6
Zeros187734
Zeros (%)96.4%
Memory size1.5 MiB
2020-09-02T20:05:47.543103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1981499297
Coefficient of variation (CV)5.335330743
Kurtosis42.49728833
Mean0.03713920266
Median Absolute Deviation (MAD)0
Skewness5.825140214
Sum7230
Variance0.03926339466
2020-09-02T20:05:47.615736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
018773496.4%
 
166853.4%
 
22260.1%
 
322< 0.1%
 
44< 0.1%
 
61< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
018773496.4%
 
166853.4%
 
22260.1%
 
322< 0.1%
 
44< 0.1%
 
51< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
61< 0.1%
 
51< 0.1%
 
44< 0.1%
 
322< 0.1%
 
22260.1%
 
166853.4%
 
018773496.4%
 

PEDCYLCOUNT
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
189189
1
 
5441
2
 
43
ValueCountFrequency (%) 
018918997.2%
 
154412.8%
 
243< 0.1%
 
2020-09-02T20:05:48.327161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
018918997.2%
 
154412.8%
 
243< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number194673100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
018918997.2%
 
154412.8%
 
243< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common194673100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
018918997.2%
 
154412.8%
 
243< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII194673100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
018918997.2%
 
154412.8%
 
243< 0.1%
 

VEHCOUNT
Real number (ℝ≥0)

ZEROS

Distinct count13
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9207799746241132
Minimum0
Maximum12
Zeros5085
Zeros (%)2.6%
Memory size1.5 MiB
2020-09-02T20:05:48.916014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6310466881
Coefficient of variation (CV)0.3285366864
Kurtosis9.051225692
Mean1.920779975
Median Absolute Deviation (MAD)0
Skewness0.5440088774
Sum373924
Variance0.3982199226
2020-09-02T20:05:48.988955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
214765075.8%
 
12574813.2%
 
3130106.7%
 
050852.6%
 
424261.2%
 
55290.3%
 
61460.1%
 
746< 0.1%
 
815< 0.1%
 
99< 0.1%
 
116< 0.1%
 
102< 0.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
050852.6%
 
12574813.2%
 
214765075.8%
 
3130106.7%
 
424261.2%
 
55290.3%
 
61460.1%
 
746< 0.1%
 
815< 0.1%
 
99< 0.1%
 
ValueCountFrequency (%) 
121< 0.1%
 
116< 0.1%
 
102< 0.1%
 
99< 0.1%
 
815< 0.1%
 
746< 0.1%
 
61460.1%
 
55290.3%
 
424261.2%
 
3130106.7%
 

INCDATE
Categorical

HIGH CARDINALITY

Distinct count5985
Unique (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2006/11/02 00:00:00+00
 
96
2008/10/03 00:00:00+00
 
92
2005/05/18 00:00:00+00
 
84
2005/11/05 00:00:00+00
 
83
2006/01/13 00:00:00+00
 
83
Other values (5980)
194235
ValueCountFrequency (%) 
2006/11/02 00:00:00+0096< 0.1%
 
2008/10/03 00:00:00+0092< 0.1%
 
2005/05/18 00:00:00+0084< 0.1%
 
2005/11/05 00:00:00+0083< 0.1%
 
2006/01/13 00:00:00+0083< 0.1%
 
2008/10/31 00:00:00+0082< 0.1%
 
2005/04/29 00:00:00+0076< 0.1%
 
2005/04/15 00:00:00+0075< 0.1%
 
2007/10/19 00:00:00+0074< 0.1%
 
2004/12/04 00:00:00+0074< 0.1%
 
2007/07/20 00:00:00+0073< 0.1%
 
2016/10/13 00:00:00+0073< 0.1%
 
2005/10/28 00:00:00+0073< 0.1%
 
2006/06/01 00:00:00+0073< 0.1%
 
2010/11/22 00:00:00+0070< 0.1%
 
2006/11/04 00:00:00+0070< 0.1%
 
2007/11/15 00:00:00+0070< 0.1%
 
2006/10/18 00:00:00+0070< 0.1%
 
2006/11/22 00:00:00+0069< 0.1%
 
2005/11/04 00:00:00+0069< 0.1%
 
2006/11/21 00:00:00+0068< 0.1%
 
2006/11/06 00:00:00+0068< 0.1%
 
2006/04/08 00:00:00+0068< 0.1%
 
2005/11/11 00:00:00+0068< 0.1%
 
2005/12/10 00:00:00+0068< 0.1%
 
Other values (5960)19280499.0%
 
2020-09-02T20:05:49.708358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories (?)4
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0208685748.7%
 
/3893469.1%
 
:3893469.1%
 
23193287.5%
 
12917976.8%
 
1946734.5%
 
+1946734.5%
 
5641041.5%
 
6623471.5%
 
7607551.4%
 
8596851.4%
 
4585551.4%
 
9557421.3%
 
3555981.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number311476872.7%
 
Other Punctuation77869218.2%
 
Space Separator1946734.5%
 
Math Symbol1946734.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0208685767.0%
 
231932810.3%
 
12917979.4%
 
5641042.1%
 
6623472.0%
 
7607552.0%
 
8596851.9%
 
4585551.9%
 
9557421.8%
 
3555981.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/38934650.0%
 
:38934650.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
194673100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+194673100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common4282806100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0208685748.7%
 
/3893469.1%
 
:3893469.1%
 
23193287.5%
 
12917976.8%
 
1946734.5%
 
+1946734.5%
 
5641041.5%
 
6623471.5%
 
7607551.4%
 
8596851.4%
 
4585551.4%
 
9557421.3%
 
3555981.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4282806100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0208685748.7%
 
/3893469.1%
 
:3893469.1%
 
23193287.5%
 
12917976.8%
 
1946734.5%
 
+1946734.5%
 
5641041.5%
 
6623471.5%
 
7607551.4%
 
8596851.4%
 
4585551.4%
 
9557421.3%
 
3555981.3%
 

INCDTTM
Categorical

HIGH CARDINALITY

Distinct count162058
Unique (%)83.2%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
11/2/2006
 
96
10/3/2008
 
91
11/5/2005
 
83
12/4/2004
 
74
6/1/2006
 
73
Other values (162053)
194256
ValueCountFrequency (%) 
11/2/200696< 0.1%
 
10/3/200891< 0.1%
 
11/5/200583< 0.1%
 
12/4/200474< 0.1%
 
6/1/200673< 0.1%
 
11/4/200670< 0.1%
 
11/4/200569< 0.1%
 
1/5/200768< 0.1%
 
5/5/200668< 0.1%
 
11/6/200668< 0.1%
 
4/8/200668< 0.1%
 
11/1/200867< 0.1%
 
11/1/200567< 0.1%
 
10/6/200665< 0.1%
 
3/8/200665< 0.1%
 
1/2/200464< 0.1%
 
11/3/200664< 0.1%
 
1/9/200664< 0.1%
 
8/6/200462< 0.1%
 
10/6/200562< 0.1%
 
7/8/200561< 0.1%
 
6/9/200561< 0.1%
 
10/2/200760< 0.1%
 
5/6/200960< 0.1%
 
4/3/200660< 0.1%
 
Other values (162033)19296399.1%
 
2020-09-02T20:05:50.717717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length22
Median length20
Mean length18.4369327
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
074987620.9%
 
139520211.0%
 
/38934610.8%
 
238303310.7%
 
3282949.1%
 
:3282949.1%
 
M1641474.6%
 
51265373.5%
 
31096743.1%
 
41096183.1%
 
P1066863.0%
 
8868182.4%
 
6867242.4%
 
7864262.4%
 
9810372.3%
 
A574611.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number221494561.7%
 
Other Punctuation71764020.0%
 
Space Separator3282949.1%
 
Uppercase Letter3282949.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
074987633.9%
 
139520217.8%
 
238303317.3%
 
51265375.7%
 
31096745.0%
 
41096184.9%
 
8868183.9%
 
6867243.9%
 
7864263.9%
 
9810373.7%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/38934654.3%
 
:32829445.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
328294100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M16414750.0%
 
P10668632.5%
 
A5746117.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common326087990.9%
 
Latin3282949.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
074987623.0%
 
139520212.1%
 
/38934611.9%
 
238303311.7%
 
32829410.1%
 
:32829410.1%
 
51265373.9%
 
31096743.4%
 
41096183.4%
 
8868182.7%
 
6867242.7%
 
7864262.7%
 
9810372.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M16414750.0%
 
P10668632.5%
 
A5746117.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3589173100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
074987620.9%
 
139520211.0%
 
/38934610.8%
 
238303310.7%
 
3282949.1%
 
:3282949.1%
 
M1641474.6%
 
51265373.5%
 
31096743.1%
 
41096183.1%
 
P1066863.0%
 
8868182.4%
 
6867242.4%
 
7864262.4%
 
9810372.3%
 
A574611.6%
 

JUNCTIONTYPE
Categorical

MISSING

Distinct count7
Unique (%)< 0.1%
Missing6329
Missing (%)3.3%
Memory size1.5 MiB
Mid-Block (not related to intersection)
89800
At Intersection (intersection related)
62810
Mid-Block (but intersection related)
22790
Driveway Junction
 
10671
At Intersection (but not related to intersection)
 
2098
Other values (2)
 
175
ValueCountFrequency (%) 
Mid-Block (not related to intersection)8980046.1%
 
At Intersection (intersection related)6281032.3%
 
Mid-Block (but intersection related)2279011.7%
 
Driveway Junction106715.5%
 
At Intersection (but not related to intersection)20981.1%
 
Ramp Junction1660.1%
 
Unknown9< 0.1%
 
(Missing)63293.3%
 
2020-09-02T20:05:51.432133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length49
Median length38
Mean length36.03394924
Min length3

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories (?)6
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t94673913.5%
 
e85047912.1%
 
6394259.1%
 
n6110698.7%
 
i5540027.9%
 
o5496387.8%
 
r4305756.1%
 
c3658335.2%
 
l2900884.1%
 
d2900884.1%
 
s2424063.5%
 
a1946642.8%
 
(1774982.5%
 
)1774982.5%
 
k1125991.6%
 
M1125901.6%
 
-1125901.6%
 
B1125901.6%
 
A649080.9%
 
I649080.9%
 
u357250.5%
 
b248880.4%
 
J108370.2%
 
w106800.2%
 
D106710.2%
 
Other values (6)218490.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter553114778.8%
 
Space Separator6394259.1%
 
Uppercase Letter3766795.4%
 
Open Punctuation1774982.5%
 
Close Punctuation1774982.5%
 
Dash Punctuation1125901.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M11259029.9%
 
B11259029.9%
 
A6490817.2%
 
I6490817.2%
 
J108372.9%
 
D106712.8%
 
R166< 0.1%
 
U9< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t94673917.1%
 
e85047915.4%
 
n61106911.0%
 
i55400210.0%
 
o5496389.9%
 
r4305757.8%
 
c3658336.6%
 
l2900885.2%
 
d2900885.2%
 
s2424064.4%
 
a1946643.5%
 
k1125992.0%
 
u357250.6%
 
b248880.4%
 
w106800.2%
 
v106710.2%
 
y106710.2%
 
m166< 0.1%
 
p166< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
639425100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(177498100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)177498100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-112590100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin590782684.2%
 
Common110701115.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t94673916.0%
 
e85047914.4%
 
n61106910.3%
 
i5540029.4%
 
o5496389.3%
 
r4305757.3%
 
c3658336.2%
 
l2900884.9%
 
d2900884.9%
 
s2424064.1%
 
a1946643.3%
 
k1125991.9%
 
M1125901.9%
 
B1125901.9%
 
A649081.1%
 
I649081.1%
 
u357250.6%
 
b248880.4%
 
J108370.2%
 
w106800.2%
 
D106710.2%
 
v106710.2%
 
y106710.2%
 
R166< 0.1%
 
m166< 0.1%
 
Other values (2)175< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
63942557.8%
 
(17749816.0%
 
)17749816.0%
 
-11259010.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7014837100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t94673913.5%
 
e85047912.1%
 
6394259.1%
 
n6110698.7%
 
i5540027.9%
 
o5496387.8%
 
r4305756.1%
 
c3658335.2%
 
l2900884.1%
 
d2900884.1%
 
s2424063.5%
 
a1946642.8%
 
(1774982.5%
 
)1774982.5%
 
k1125991.6%
 
M1125901.6%
 
-1125901.6%
 
B1125901.6%
 
A649080.9%
 
I649080.9%
 
u357250.5%
 
b248880.4%
 
J108370.2%
 
w106800.2%
 
D106710.2%
 
Other values (6)218490.3%
 

SDOT_COLCODE
Real number (ℝ≥0)

ZEROS

Distinct count39
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.867768000698607
Minimum0
Maximum69
Zeros9787
Zeros (%)5.0%
Memory size1.5 MiB
2020-09-02T20:05:51.509661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median13
Q314
95-th percentile28
Maximum69
Range69
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.86875462
Coefficient of variation (CV)0.4953035427
Kurtosis11.0243977
Mean13.867768
Median Absolute Deviation (MAD)2
Skewness2.235546333
Sum2699680
Variance47.17979003
2020-09-02T20:05:51.584715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
118520943.8%
 
145429927.9%
 
1699285.1%
 
097875.0%
 
2888564.5%
 
2465183.3%
 
1358523.0%
 
2647412.4%
 
1831041.6%
 
1516040.8%
 
1214400.7%
 
5113120.7%
 
294790.2%
 
211810.1%
 
561800.1%
 
271660.1%
 
541390.1%
 
231240.1%
 
481070.1%
 
311040.1%
 
251020.1%
 
3493< 0.1%
 
6475< 0.1%
 
6969< 0.1%
 
3353< 0.1%
 
Other values (14)1510.1%
 
ValueCountFrequency (%) 
097875.0%
 
118520943.8%
 
1214400.7%
 
1358523.0%
 
145429927.9%
 
1516040.8%
 
1699285.1%
 
1831041.6%
 
211810.1%
 
2217< 0.1%
 
ValueCountFrequency (%) 
6969< 0.1%
 
684< 0.1%
 
6623< 0.1%
 
6475< 0.1%
 
617< 0.1%
 
585< 0.1%
 
561800.1%
 
5550< 0.1%
 
541390.1%
 
539< 0.1%
 

SDOT_COLDESC
Categorical

Distinct count39
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLE
85209
MOTOR VEHICLE STRUCK MOTOR VEHICLE, REAR END
54299
MOTOR VEHICLE STRUCK MOTOR VEHICLE, LEFT SIDE SIDESWIPE
 
9928
NOT ENOUGH INFORMATION / NOT APPLICABLE
 
9787
MOTOR VEHICLE RAN OFF ROAD - HIT FIXED OBJECT
 
8856
Other values (34)
26594
ValueCountFrequency (%) 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLE8520943.8%
 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, REAR END5429927.9%
 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, LEFT SIDE SIDESWIPE99285.1%
 
NOT ENOUGH INFORMATION / NOT APPLICABLE97875.0%
 
MOTOR VEHICLE RAN OFF ROAD - HIT FIXED OBJECT88564.5%
 
MOTOR VEHCILE STRUCK PEDESTRIAN65183.3%
 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, LEFT SIDE AT ANGLE58523.0%
 
MOTOR VEHICLE STRUCK OBJECT IN ROAD47412.4%
 
MOTOR VEHICLE STRUCK PEDALCYCLIST, FRONT END AT ANGLE31041.6%
 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, RIGHT SIDE SIDESWIPE16040.8%
 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, RIGHT SIDE AT ANGLE14400.7%
 
PEDALCYCLIST STRUCK MOTOR VEHICLE FRONT END AT ANGLE13120.7%
 
MOTOR VEHICLE OVERTURNED IN ROAD4790.2%
 
MOTOR VEHICLE STRUCK PEDALCYCLIST, REAR END1810.1%
 
PEDALCYCLIST STRUCK MOTOR VEHICLE LEFT SIDE SIDESWIPE1800.1%
 
MOTOR VEHICLE RAN OFF ROAD - NO COLLISION1660.1%
 
PEDALCYCLIST STRUCK MOTOR VEHICLE REAR END1390.1%
 
MOTOR VEHICLE STRUCK PEDALCYCLIST, LEFT SIDE SIDESWIPE1240.1%
 
DRIVERLESS VEHICLE RAN OFF ROAD - HIT FIXED OBJECT1070.1%
 
DRIVERLESS VEHICLE STRUCK MOTOR VEHICLE FRONT END AT ANGLE1040.1%
 
MOTOR VEHICLE STRUCK TRAIN1020.1%
 
DRIVERLESS VEHICLE STRUCK MOTOR VEHICLE REAR END93< 0.1%
 
PEDALCYCLIST STRUCK PEDESTRIAN75< 0.1%
 
PEDALCYCLIST OVERTURNED IN ROAD69< 0.1%
 
DRIVERLESS VEHICLE STRUCK MOTOR VEHICLE LEFT SIDE AT ANGLE53< 0.1%
 
Other values (14)1510.1%
 
2020-09-02T20:05:52.298790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length60
Median length54
Mean length48.73760614
Min length26

Overview of Unicode Properties

Unique unicode characters28
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
135119614.2%
 
E110481311.6%
 
O8628679.1%
 
T7888638.3%
 
R7623748.0%
 
C5528255.8%
 
L4874845.1%
 
I4548964.8%
 
N4022584.2%
 
H3651923.8%
 
M3527033.7%
 
V3442463.6%
 
A3138863.3%
 
S2311742.4%
 
D2119162.2%
 
U1855392.0%
 
K1752041.8%
 
,1617581.7%
 
F1429061.5%
 
G1100201.2%
 
P434010.5%
 
B235210.2%
 
J137340.1%
 
W119160.1%
 
/97870.1%
 
Other values (3)234170.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter795602183.9%
 
Space Separator135119614.2%
 
Other Punctuation1715451.8%
 
Dash Punctuation91340.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E110481313.9%
 
O86286710.8%
 
T7888639.9%
 
R7623749.6%
 
C5528256.9%
 
L4874846.1%
 
I4548965.7%
 
N4022585.1%
 
H3651924.6%
 
M3527034.4%
 
V3442464.3%
 
A3138863.9%
 
S2311742.9%
 
D2119162.7%
 
U1855392.3%
 
K1752042.2%
 
F1429061.8%
 
G1100201.4%
 
P434010.5%
 
B235210.3%
 
J137340.2%
 
W119160.1%
 
X89670.1%
 
Y53160.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1351196100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,16175894.3%
 
/97875.7%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-9134100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin795602183.9%
 
Common153187516.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E110481313.9%
 
O86286710.8%
 
T7888639.9%
 
R7623749.6%
 
C5528256.9%
 
L4874846.1%
 
I4548965.7%
 
N4022585.1%
 
H3651924.6%
 
M3527034.4%
 
V3442464.3%
 
A3138863.9%
 
S2311742.9%
 
D2119162.7%
 
U1855392.3%
 
K1752042.2%
 
F1429061.8%
 
G1100201.4%
 
P434010.5%
 
B235210.3%
 
J137340.2%
 
W119160.1%
 
X89670.1%
 
Y53160.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
135119688.2%
 
,16175810.6%
 
/97870.6%
 
-91340.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9487896100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
135119614.2%
 
E110481311.6%
 
O8628679.1%
 
T7888638.3%
 
R7623748.0%
 
C5528255.8%
 
L4874845.1%
 
I4548964.8%
 
N4022584.2%
 
H3651923.8%
 
M3527033.7%
 
V3442463.6%
 
A3138863.3%
 
S2311742.4%
 
D2119162.2%
 
U1855392.0%
 
K1752041.8%
 
,1617581.7%
 
F1429061.5%
 
G1100201.2%
 
P434010.5%
 
B235210.2%
 
J137340.1%
 
W119160.1%
 
/97870.1%
 
Other values (3)234170.2%
 

INATTENTIONIND
Categorical

MISSING

Distinct count1
Unique (%)< 0.1%
Missing164868
Missing (%)84.7%
Memory size1.5 MiB
Y
29805
ValueCountFrequency (%) 
Y2980515.3%
 
(Missing)16486884.7%
 
2020-09-02T20:05:53.024738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.693794209
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n32973662.9%
 
a16486831.4%
 
Y298055.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter49460494.3%
 
Uppercase Letter298055.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n32973666.7%
 
a16486833.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y29805100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin524409100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n32973662.9%
 
a16486831.4%
 
Y298055.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII524409100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n32973662.9%
 
a16486831.4%
 
Y298055.7%
 

UNDERINFL
Categorical

MISSING

Distinct count4
Unique (%)< 0.1%
Missing4884
Missing (%)2.5%
Memory size1.5 MiB
N
100274
0
80394
Y
 
5126
1
 
3995
ValueCountFrequency (%) 
N10027451.5%
 
08039441.3%
 
Y51262.6%
 
139952.1%
 
(Missing)48842.5%
 
2020-09-02T20:05:53.757225image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length1
Mean length1.05017645
Min length1

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories (?)3
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10027449.0%
 
08039439.3%
 
n97684.8%
 
Y51262.5%
 
a48842.4%
 
139952.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter10540051.6%
 
Decimal Number8438941.3%
 
Lowercase Letter146527.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10027495.1%
 
Y51264.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
08039495.3%
 
139954.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n976866.7%
 
a488433.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin12005258.7%
 
Common8438941.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10027483.5%
 
n97688.1%
 
Y51264.3%
 
a48844.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
08039495.3%
 
139954.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII204441100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10027449.0%
 
08039439.3%
 
n97684.8%
 
Y51262.5%
 
a48842.4%
 
139952.0%
 

WEATHER
Categorical

MISSING

Distinct count11
Unique (%)< 0.1%
Missing5081
Missing (%)2.6%
Memory size1.5 MiB
Clear
111135
Raining
33145
Overcast
27714
Unknown
 
15091
Snowing
 
907
Other values (6)
 
1600
ValueCountFrequency (%) 
Clear11113557.1%
 
Raining3314517.0%
 
Overcast2771414.2%
 
Unknown150917.8%
 
Snowing9070.5%
 
Other8320.4%
 
Fog/Smog/Smoke5690.3%
 
Sleet/Hail/Freezing Rain1130.1%
 
Blowing Sand/Dirt56< 0.1%
 
Severe Crosswind25< 0.1%
 
Partly Cloudy5< 0.1%
 
(Missing)50812.6%
 
2020-09-02T20:05:54.474804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length24
Median length5
Mean length5.922166916
Min length3

Overview of Unicode Properties

Unique unicode characters32
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a17736215.4%
 
e14077712.2%
 
r13990512.1%
 
n12390210.7%
 
l1114279.7%
 
C1111659.6%
 
i676735.9%
 
g353593.1%
 
R332582.9%
 
t287202.5%
 
O285462.5%
 
s277642.4%
 
v277392.4%
 
c277142.4%
 
o177911.5%
 
w160791.4%
 
k156601.4%
 
U150911.3%
 
S22390.2%
 
/14200.1%
 
m11380.1%
 
h8320.1%
 
F6820.1%
 
199< 0.1%
 
H113< 0.1%
 
Other values (7)331< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter96005683.3%
 
Uppercase Letter19121116.6%
 
Other Punctuation14200.1%
 
Space Separator199< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C11116558.1%
 
R3325817.4%
 
O2854614.9%
 
U150917.9%
 
S22391.2%
 
F6820.4%
 
H1130.1%
 
B56< 0.1%
 
D56< 0.1%
 
P5< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a17736218.5%
 
e14077714.7%
 
r13990514.6%
 
n12390212.9%
 
l11142711.6%
 
i676737.0%
 
g353593.7%
 
t287203.0%
 
s277642.9%
 
v277392.9%
 
c277142.9%
 
o177911.9%
 
w160791.7%
 
k156601.6%
 
m11380.1%
 
h8320.1%
 
z113< 0.1%
 
d86< 0.1%
 
y10< 0.1%
 
u5< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/1420100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
199100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin115126799.9%
 
Common16190.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a17736215.4%
 
e14077712.2%
 
r13990512.2%
 
n12390210.8%
 
l1114279.7%
 
C1111659.7%
 
i676735.9%
 
g353593.1%
 
R332582.9%
 
t287202.5%
 
O285462.5%
 
s277642.4%
 
v277392.4%
 
c277142.4%
 
o177911.5%
 
w160791.4%
 
k156601.4%
 
U150911.3%
 
S22390.2%
 
m11380.1%
 
h8320.1%
 
F6820.1%
 
H113< 0.1%
 
z113< 0.1%
 
d86< 0.1%
 
Other values (5)132< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
/142087.7%
 
19912.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1152886100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a17736215.4%
 
e14077712.2%
 
r13990512.1%
 
n12390210.7%
 
l1114279.7%
 
C1111659.6%
 
i676735.9%
 
g353593.1%
 
R332582.9%
 
t287202.5%
 
O285462.5%
 
s277642.4%
 
v277392.4%
 
c277142.4%
 
o177911.5%
 
w160791.4%
 
k156601.4%
 
U150911.3%
 
S22390.2%
 
/14200.1%
 
m11380.1%
 
h8320.1%
 
F6820.1%
 
199< 0.1%
 
H113< 0.1%
 
Other values (7)331< 0.1%
 

ROADCOND
Categorical

MISSING

Distinct count9
Unique (%)< 0.1%
Missing5012
Missing (%)2.6%
Memory size1.5 MiB
Dry
124510
Wet
47474
Unknown
 
15078
Ice
 
1209
Snow/Slush
 
1004
Other values (4)
 
386
ValueCountFrequency (%) 
Dry12451064.0%
 
Wet4747424.4%
 
Unknown150787.7%
 
Ice12090.6%
 
Snow/Slush10040.5%
 
Other1320.1%
 
Standing Water1150.1%
 
Sand/Mud/Dirt75< 0.1%
 
Oil64< 0.1%
 
(Missing)50122.6%
 
2020-09-02T20:05:55.190423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length14
Median length3
Mean length3.357620214
Min length3

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r12483219.1%
 
D12458519.1%
 
y12451019.0%
 
n565678.7%
 
e489307.5%
 
t479117.3%
 
W475897.3%
 
o160822.5%
 
w160822.5%
 
U150782.3%
 
k150782.3%
 
a53170.8%
 
S21980.3%
 
I12090.2%
 
c12090.2%
 
/11540.2%
 
h11360.2%
 
u10790.2%
 
l10680.2%
 
s10040.2%
 
d265< 0.1%
 
i254< 0.1%
 
O196< 0.1%
 
g115< 0.1%
 
115< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter46143970.6%
 
Uppercase Letter19093029.2%
 
Other Punctuation11540.2%
 
Space Separator115< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D12458565.3%
 
W4758924.9%
 
U150787.9%
 
S21981.2%
 
I12090.6%
 
O1960.1%
 
M75< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r12483227.1%
 
y12451027.0%
 
n5656712.3%
 
e4893010.6%
 
t4791110.4%
 
o160823.5%
 
w160823.5%
 
k150783.3%
 
a53171.2%
 
c12090.3%
 
h11360.2%
 
u10790.2%
 
l10680.2%
 
s10040.2%
 
d2650.1%
 
i2540.1%
 
g115< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/1154100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
115100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin65236999.8%
 
Common12690.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r12483219.1%
 
D12458519.1%
 
y12451019.1%
 
n565678.7%
 
e489307.5%
 
t479117.3%
 
W475897.3%
 
o160822.5%
 
w160822.5%
 
U150782.3%
 
k150782.3%
 
a53170.8%
 
S21980.3%
 
I12090.2%
 
c12090.2%
 
h11360.2%
 
u10790.2%
 
l10680.2%
 
s10040.2%
 
d265< 0.1%
 
i254< 0.1%
 
O196< 0.1%
 
g115< 0.1%
 
M75< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
/115490.9%
 
1159.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII653638100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r12483219.1%
 
D12458519.1%
 
y12451019.0%
 
n565678.7%
 
e489307.5%
 
t479117.3%
 
W475897.3%
 
o160822.5%
 
w160822.5%
 
U150782.3%
 
k150782.3%
 
a53170.8%
 
S21980.3%
 
I12090.2%
 
c12090.2%
 
/11540.2%
 
h11360.2%
 
u10790.2%
 
l10680.2%
 
s10040.2%
 
d265< 0.1%
 
i254< 0.1%
 
O196< 0.1%
 
g115< 0.1%
 
115< 0.1%
 

LIGHTCOND
Categorical

MISSING

Distinct count9
Unique (%)< 0.1%
Missing5170
Missing (%)2.7%
Memory size1.5 MiB
Daylight
116137
Dark - Street Lights On
48507
Unknown
 
13473
Dusk
 
5902
Dawn
 
2502
Other values (4)
 
2982
ValueCountFrequency (%) 
Daylight11613759.7%
 
Dark - Street Lights On4850724.9%
 
Unknown134736.9%
 
Dusk59023.0%
 
Dawn25021.3%
 
Dark - No Street Lights15370.8%
 
Dark - Street Lights Off11990.6%
 
Other2350.1%
 
Dark - Unknown Lighting11< 0.1%
 
(Missing)51702.7%
 
2020-09-02T20:05:55.903599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length24
Median length8
Mean length11.57710109
Min length3

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t27011212.0%
 
2050059.1%
 
D1757957.8%
 
a1750637.8%
 
h1676267.4%
 
i1674027.4%
 
g1674027.4%
 
y1161375.2%
 
l1161375.2%
 
r1027324.6%
 
e1027214.6%
 
n1018124.5%
 
k706403.1%
 
s571452.5%
 
-512542.3%
 
L512542.3%
 
S512432.3%
 
O499412.2%
 
w159860.7%
 
o150210.7%
 
U134840.6%
 
u59020.3%
 
f23980.1%
 
N15370.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter165423673.4%
 
Uppercase Letter34325415.2%
 
Space Separator2050059.1%
 
Dash Punctuation512542.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D17579551.2%
 
L5125414.9%
 
S5124314.9%
 
O4994114.5%
 
U134843.9%
 
N15370.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t27011216.3%
 
a17506310.6%
 
h16762610.1%
 
i16740210.1%
 
g16740210.1%
 
y1161377.0%
 
l1161377.0%
 
r1027326.2%
 
e1027216.2%
 
n1018126.2%
 
k706404.3%
 
s571453.5%
 
w159861.0%
 
o150210.9%
 
u59020.4%
 
f23980.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
205005100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-51254100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin199749088.6%
 
Common25625911.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t27011213.5%
 
D1757958.8%
 
a1750638.8%
 
h1676268.4%
 
i1674028.4%
 
g1674028.4%
 
y1161375.8%
 
l1161375.8%
 
r1027325.1%
 
e1027215.1%
 
n1018125.1%
 
k706403.5%
 
s571452.9%
 
L512542.6%
 
S512432.6%
 
O499412.5%
 
w159860.8%
 
o150210.8%
 
U134840.7%
 
u59020.3%
 
f23980.1%
 
N15370.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
20500580.0%
 
-5125420.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2253749100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t27011212.0%
 
2050059.1%
 
D1757957.8%
 
a1750637.8%
 
h1676267.4%
 
i1674027.4%
 
g1674027.4%
 
y1161375.2%
 
l1161375.2%
 
r1027324.6%
 
e1027214.6%
 
n1018124.5%
 
k706403.1%
 
s571452.5%
 
-512542.3%
 
L512542.3%
 
S512432.3%
 
O499412.2%
 
w159860.7%
 
o150210.7%
 
U134840.6%
 
u59020.3%
 
f23980.1%
 
N15370.1%
 

PEDROWNOTGRNT
Categorical

MISSING

Distinct count1
Unique (%)< 0.1%
Missing190006
Missing (%)97.6%
Memory size1.5 MiB
Y
4667
ValueCountFrequency (%) 
Y46672.4%
 
(Missing)19000697.6%
 
2020-09-02T20:05:56.625846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.95205293
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n38001266.1%
 
a19000633.1%
 
Y46670.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter57001899.2%
 
Uppercase Letter46670.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n38001266.7%
 
a19000633.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y4667100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin574685100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n38001266.1%
 
a19000633.1%
 
Y46670.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII574685100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n38001266.1%
 
a19000633.1%
 
Y46670.8%
 

SDOTCOLNUM
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct count114932
Unique (%)> 99.9%
Missing79737
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean7972521.3371441495
Minimum1007024.0
Maximum13072024.0
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-09-02T20:05:56.742153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1007024
5-th percentile4169040.75
Q16040014.75
median8023022.5
Q310155010.25
95-th percentile12224003.25
Maximum13072024
Range12065000
Interquartile range (IQR)4114995.5

Descriptive statistics

Standard deviation2553533.452
Coefficient of variation (CV)0.3202918304
Kurtosis-1.091997543
Mean7972521.337
Median Absolute Deviation (MAD)2068995
Skewness0.2084355443
Sum9.163297124e+11
Variance6.520533089e+12
2020-09-02T20:05:56.809483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
41120252< 0.1%
 
41160342< 0.1%
 
41160482< 0.1%
 
112000072< 0.1%
 
110500131< 0.1%
 
63450191< 0.1%
 
120300051< 0.1%
 
50360231< 0.1%
 
101610071< 0.1%
 
40280361< 0.1%
 
70870081< 0.1%
 
120040521< 0.1%
 
101610181< 0.1%
 
120270221< 0.1%
 
50360111< 0.1%
 
103420271< 0.1%
 
50360031< 0.1%
 
102040331< 0.1%
 
60780221< 0.1%
 
102780101< 0.1%
 
60780101< 0.1%
 
70870391< 0.1%
 
72190041< 0.1%
 
102090351< 0.1%
 
60780071< 0.1%
 
Other values (114907)11490759.0%
 
(Missing)7973741.0%
 
ValueCountFrequency (%) 
10070241< 0.1%
 
31370161< 0.1%
 
32390351< 0.1%
 
40010011< 0.1%
 
40010021< 0.1%
 
40010031< 0.1%
 
40010041< 0.1%
 
40010051< 0.1%
 
40010061< 0.1%
 
40010071< 0.1%
 
ValueCountFrequency (%) 
130720241< 0.1%
 
130720231< 0.1%
 
130720221< 0.1%
 
130720211< 0.1%
 
130720201< 0.1%
 
130720191< 0.1%
 
130720181< 0.1%
 
130720171< 0.1%
 
130720161< 0.1%
 
130720151< 0.1%
 

SPEEDING
Categorical

MISSING

Distinct count1
Unique (%)< 0.1%
Missing185340
Missing (%)95.2%
Memory size1.5 MiB
Y
9333
ValueCountFrequency (%) 
Y93334.8%
 
(Missing)18534095.2%
 
2020-09-02T20:05:57.526131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.904116133
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n37068065.6%
 
a18534032.8%
 
Y93331.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter55602098.3%
 
Uppercase Letter93331.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n37068066.7%
 
a18534033.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y9333100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin565353100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n37068065.6%
 
a18534032.8%
 
Y93331.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII565353100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n37068065.6%
 
a18534032.8%
 
Y93331.7%
 

ST_COLCODE
Unsupported

REJECTED
UNSUPPORTED

Missing18
Missing (%)< 0.1%
Memory size9.5 MiB

ST_COLDESC
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct count62
Unique (%)< 0.1%
Missing4904
Missing (%)2.5%
Memory size1.5 MiB
One parked--one moving
44421
Entering at angle
34674
From same direction - both going straight - one stopped - rear-end
25771
Fixed object
13554
From same direction - both going straight - both moving - sideswipe
12777
Other values (57)
58572
ValueCountFrequency (%) 
One parked--one moving4442122.8%
 
Entering at angle3467417.8%
 
From same direction - both going straight - one stopped - rear-end2577113.2%
 
Fixed object135547.0%
 
From same direction - both going straight - both moving - sideswipe127776.6%
 
From opposite direction - one left turn - one straight103245.3%
 
From same direction - both going straight - both moving - rear-end76293.9%
 
Vehicle - Pedalcyclist47012.4%
 
From same direction - all others45372.3%
 
From same direction - one left turn - one straight30931.6%
 
From same direction - one right turn - one straight29561.5%
 
Vehicle going straight hits pedestrian28821.5%
 
One car leaving parked position28461.5%
 
From same direction - both going straight - one stopped - sideswipe24351.3%
 
One car leaving driveway access22741.2%
 
Vehicle turning left hits pedestrian21781.1%
 
One car entering driveway access16170.8%
 
From opposite direction - all others13020.7%
 
Vehicle turning right hits pedestrian12010.6%
 
Same direction -- both turning right -- both moving -- sideswipe11840.6%
 
From opposite direction - both going straight - sideswipe10390.5%
 
Same direction -- both turning left -- both moving -- sideswipe8350.4%
 
Vehicle overturned8150.4%
 
One car entering parked position7200.4%
 
From opposite direction - both moving - head-on5900.3%
 
Other values (37)34141.8%
 
(Missing)49042.5%
 
2020-09-02T20:05:58.245984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length85
Median length22
Mean length35.80246362
Min length3

Overview of Unicode Properties

Unique unicode characters49
Unique unicode categories (?)7
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
100175014.4%
 
e6801049.8%
 
n5553698.0%
 
o5392797.7%
 
i5060377.3%
 
t4964587.1%
 
r4284666.1%
 
-3340204.8%
 
g3321924.8%
 
a3276434.7%
 
s2459203.5%
 
d2370433.4%
 
m2029992.9%
 
h1786612.6%
 
p1628492.3%
 
c1300191.9%
 
l936231.3%
 
b901721.3%
 
F866631.2%
 
v778851.1%
 
O524680.8%
 
k491730.7%
 
E347430.5%
 
u242340.3%
 
w226990.3%
 
Other values (24)793041.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter543526478.0%
 
Space Separator100175014.4%
 
Dash Punctuation3340204.8%
 
Uppercase Letter1985702.8%
 
Other Punctuation93< 0.1%
 
Open Punctuation38< 0.1%
 
Close Punctuation38< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F8666343.6%
 
O5246826.4%
 
E3474317.5%
 
V132376.7%
 
P56522.8%
 
S38021.9%
 
A4250.2%
 
M3470.2%
 
R2780.1%
 
C2350.1%
 
L1290.1%
 
N1240.1%
 
I92< 0.1%
 
T92< 0.1%
 
D81< 0.1%
 
Y69< 0.1%
 
H62< 0.1%
 
B34< 0.1%
 
U23< 0.1%
 
W14< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e68010412.5%
 
n55536910.2%
 
o5392799.9%
 
i5060379.3%
 
t4964589.1%
 
r4284667.9%
 
g3321926.1%
 
a3276436.0%
 
s2459204.5%
 
d2370434.4%
 
m2029993.7%
 
h1786613.3%
 
p1628493.0%
 
c1300192.4%
 
l936231.7%
 
b901721.7%
 
v778851.4%
 
k491730.9%
 
u242340.4%
 
w226990.4%
 
f171130.3%
 
j139940.3%
 
x135540.2%
 
y97780.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1001750100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-334020100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(38100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,93100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)38100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin563383480.8%
 
Common133593919.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e68010412.1%
 
n5553699.9%
 
o5392799.6%
 
i5060379.0%
 
t4964588.8%
 
r4284667.6%
 
g3321925.9%
 
a3276435.8%
 
s2459204.4%
 
d2370434.2%
 
m2029993.6%
 
h1786613.2%
 
p1628492.9%
 
c1300192.3%
 
l936231.7%
 
b901721.6%
 
F866631.5%
 
v778851.4%
 
O524680.9%
 
k491730.9%
 
E347430.6%
 
u242340.4%
 
w226990.4%
 
f171130.3%
 
j139940.2%
 
Other values (19)480280.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
100175075.0%
 
-33402025.0%
 
,93< 0.1%
 
(38< 0.1%
 
)38< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6969773100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
100175014.4%
 
e6801049.8%
 
n5553698.0%
 
o5392797.7%
 
i5060377.3%
 
t4964587.1%
 
r4284666.1%
 
-3340204.8%
 
g3321924.8%
 
a3276434.7%
 
s2459203.5%
 
d2370433.4%
 
m2029992.9%
 
h1786612.6%
 
p1628492.3%
 
c1300191.9%
 
l936231.3%
 
b901721.3%
 
F866631.2%
 
v778851.1%
 
O524680.8%
 
k491730.7%
 
E347430.5%
 
u242340.3%
 
w226990.3%
 
Other values (24)793041.1%
 

SEGLANEKEY
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count1955
Unique (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.40111366239796
Minimum0
Maximum525241
Zeros191907
Zeros (%)98.6%
Memory size1.5 MiB
2020-09-02T20:05:58.321912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum525241
Range525241
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3315.776055
Coefficient of variation (CV)12.30795229
Kurtosis9639.267978
Mean269.4011137
Median Absolute Deviation (MAD)0
Skewness66.46373104
Sum52445123
Variance10994370.85
2020-09-02T20:05:58.394082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
019190798.6%
 
653219< 0.1%
 
607816< 0.1%
 
1216215< 0.1%
 
1033614< 0.1%
 
1034213< 0.1%
 
898512< 0.1%
 
1035410< 0.1%
 
1042010< 0.1%
 
881610< 0.1%
 
1217910< 0.1%
 
103689< 0.1%
 
105908< 0.1%
 
89958< 0.1%
 
107738< 0.1%
 
427777< 0.1%
 
105667< 0.1%
 
129417< 0.1%
 
103747< 0.1%
 
126496< 0.1%
 
89906< 0.1%
 
82406< 0.1%
 
120356< 0.1%
 
105326< 0.1%
 
421666< 0.1%
 
Other values (1930)25401.3%
 
ValueCountFrequency (%) 
019190798.6%
 
11891< 0.1%
 
12001< 0.1%
 
12481< 0.1%
 
12571< 0.1%
 
12711< 0.1%
 
13091< 0.1%
 
13501< 0.1%
 
13711< 0.1%
 
14081< 0.1%
 
ValueCountFrequency (%) 
5252411< 0.1%
 
5251691< 0.1%
 
5211171< 0.1%
 
592601< 0.1%
 
547281< 0.1%
 
469811< 0.1%
 
458801< 0.1%
 
458321< 0.1%
 
458311< 0.1%
 
458001< 0.1%
 

CROSSWALKKEY
Real number (ℝ≥0)

ZEROS

Distinct count2198
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9782.451978445906
Minimum0
Maximum5239700
Zeros190862
Zeros (%)98.0%
Memory size1.5 MiB
2020-09-02T20:05:58.474691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5239700
Range5239700
Interquartile range (IQR)0

Descriptive statistics

Standard deviation72269.25669
Coefficient of variation (CV)7.387642367
Kurtosis188.4609925
Mean9782.451978
Median Absolute Deviation (MAD)0
Skewness8.879833967
Sum1904379274
Variance5222845463
2020-09-02T20:05:58.549452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
019086298.0%
 
52360917< 0.1%
 
52083815< 0.1%
 
52556713< 0.1%
 
52170710< 0.1%
 
52369910< 0.1%
 
5231489< 0.1%
 
5218639< 0.1%
 
5216049< 0.1%
 
5237359< 0.1%
 
5242659< 0.1%
 
5228919< 0.1%
 
5222648< 0.1%
 
5246898< 0.1%
 
5256598< 0.1%
 
5210408< 0.1%
 
5239878< 0.1%
 
5208558< 0.1%
 
5231098< 0.1%
 
5240298< 0.1%
 
5221088< 0.1%
 
5223778< 0.1%
 
5241788< 0.1%
 
5256448< 0.1%
 
5218457< 0.1%
 
Other values (2173)35891.8%
 
ValueCountFrequency (%) 
019086298.0%
 
5231< 0.1%
 
73581< 0.1%
 
90731< 0.1%
 
105901< 0.1%
 
154851< 0.1%
 
175581< 0.1%
 
212141< 0.1%
 
238601< 0.1%
 
238781< 0.1%
 
ValueCountFrequency (%) 
52397001< 0.1%
 
7034801< 0.1%
 
7013061< 0.1%
 
7012801< 0.1%
 
7011101< 0.1%
 
7005261< 0.1%
 
7003881< 0.1%
 
6998891< 0.1%
 
6998791< 0.1%
 
6998761< 0.1%
 

HITPARKEDCAR
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
N
187457
Y
 
7216
ValueCountFrequency (%) 
N18745796.3%
 
Y72163.7%
 

Interactions

2020-09-02T20:05:11.537927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:11.683016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:11.817287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:11.952456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:12.092045image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:12.214822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:12.354451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:16.673296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:18.811047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:19.071040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:19.207626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:19.982834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:20.836155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:23.697783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:23.947192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:24.086523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:24.218482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:24.344441image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:24.473422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:27.805920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:28.194528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:28.598713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:28.730471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:28.857244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:28.988578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:29.958486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:30.082010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:30.200059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:30.316793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:32.194112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-02T20:05:33.837279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:33.962440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-02T20:05:58.646904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-02T20:05:58.823289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-02T20:05:58.998481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-02T20:05:59.195440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-02T20:05:59.433215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-02T20:05:34.829641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:36.300610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:37.701255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-02T20:05:38.267483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

SEVERITYCODEXYOBJECTIDINCKEYCOLDETKEYREPORTNOSTATUSADDRTYPEINTKEYLOCATIONEXCEPTRSNCODEEXCEPTRSNDESCSEVERITYCODE.1SEVERITYDESCCOLLISIONTYPEPERSONCOUNTPEDCOUNTPEDCYLCOUNTVEHCOUNTINCDATEINCDTTMJUNCTIONTYPESDOT_COLCODESDOT_COLDESCINATTENTIONINDUNDERINFLWEATHERROADCONDLIGHTCONDPEDROWNOTGRNTSDOTCOLNUMSPEEDINGST_COLCODEST_COLDESCSEGLANEKEYCROSSWALKKEYHITPARKEDCAR
02-122.32314847.7031401130713073502005MatchedIntersection37475.05TH AVE NE AND NE 103RD STNaN2Injury CollisionAngles20022013/03/27 00:00:00+003/27/2013 2:54:00 PMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNOvercastWetDaylightNaNNaNNaN10Entering at angle00N
11-122.34729447.647172252200522002607959MatchedBlockNaNAURORA BR BETWEEN RAYE ST AND BRIDGE WAY NNaNNaN1Property Damage Only CollisionSideswipe20022006/12/20 00:00:00+0012/20/2006 6:55:00 PMMid-Block (not related to intersection)16MOTOR VEHICLE STRUCK MOTOR VEHICLE, LEFT SIDE SIDESWIPENaN0RainingWetDark - Street Lights OnNaN6354039.0NaN11From same direction - both going straight - both moving - sideswipe00N
21-122.33454047.607871326700267001482393MatchedBlockNaN4TH AVE BETWEEN SENECA ST AND UNIVERSITY STNaNNaN1Property Damage Only CollisionParked Car40032004/11/18 00:00:00+0011/18/2004 10:20:00 AMMid-Block (not related to intersection)14MOTOR VEHICLE STRUCK MOTOR VEHICLE, REAR ENDNaN0OvercastDryDaylightNaN4323031.0NaN32One parked--one moving00N
31-122.33480347.6048034114411443503937MatchedBlockNaN2ND AVE BETWEEN MARION ST AND MADISON STNaN1Property Damage Only CollisionOther30032013/03/29 00:00:00+003/29/2013 9:26:00 AMMid-Block (not related to intersection)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN23From same direction - all others00N
42-122.30642647.545739517700177001807429MatchedIntersection34387.0SWIFT AVE S AND SWIFT AV OFF RPNaNNaN2Injury CollisionAngles20022004/01/28 00:00:00+001/28/2004 8:04:00 AMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaN0RainingWetDaylightNaN4028032.0NaN10Entering at angle00N
51-122.38759847.6905756320840322340E919477MatchedIntersection36974.024TH AVE NW AND NW 85TH STNaN1Property Damage Only CollisionAngles20022019/04/20 00:00:00+004/20/2019 5:42:00 PMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN10Entering at angle00N
61-122.33848547.618534783300833003282542MatchedIntersection29510.0DENNY WAY AND WESTLAKE AVENaNNaN1Property Damage Only CollisionAngles20022008/12/09 00:00:00+0012/9/2008At Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaN0RainingWetDaylightNaN8344002.0NaN10Entering at angle00N
72-122.32078047.6140769330897332397EA30304MatchedIntersection29745.0BROADWAY AND E PIKE STNaN2Injury CollisionCycles30112020/04/15 00:00:00+004/15/2020 5:47:00 PMAt Intersection (intersection related)51PEDALCYCLIST STRUCK MOTOR VEHICLE FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN5Vehicle Strikes Pedalcyclist68550N
81-122.33593047.6119041063400634002071243MatchedBlockNaNPINE ST BETWEEN 5TH AVE AND 6TH AVENaNNaN1Property Damage Only CollisionParked Car20022006/06/15 00:00:00+006/15/2006 1:00:00 PMMid-Block (not related to intersection)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaN0ClearDryDaylightNaN6166014.0NaN32One parked--one moving00N
92-122.38470047.5284751258600586002072105MatchedIntersection34679.041ST AVE SW AND SW THISTLE STNaNNaN2Injury CollisionAngles20022006/03/20 00:00:00+003/20/2006 3:49:00 PMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaN0ClearDryDaylightNaN6079001.0NaN10Entering at angle00N

Last rows

SEVERITYCODEXYOBJECTIDINCKEYCOLDETKEYREPORTNOSTATUSADDRTYPEINTKEYLOCATIONEXCEPTRSNCODEEXCEPTRSNDESCSEVERITYCODE.1SEVERITYDESCCOLLISIONTYPEPERSONCOUNTPEDCOUNTPEDCYLCOUNTVEHCOUNTINCDATEINCDTTMJUNCTIONTYPESDOT_COLCODESDOT_COLDESCINATTENTIONINDUNDERINFLWEATHERROADCONDLIGHTCONDPEDROWNOTGRNTSDOTCOLNUMSPEEDINGST_COLCODEST_COLDESCSEGLANEKEYCROSSWALKKEYHITPARKEDCAR
1946632-122.29916047.579673219536309335310615E880807MatchedBlockNaNRAINIER AVE S BETWEEN S BAYVIEW ST AND S MCCLELLAN STNaN2Injury CollisionAngles30022019/01/09 00:00:00+001/9/2019 12:51:00 PMMid-Block (not related to intersection)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLEYNRainingWetDaylightNaNNaNNaN10Entering at angle00N
1946641-122.32588747.643191219537309222310502E879537MatchedIntersection28300.0EASTLAKE AVE E AND E ROANOKE STNaN1Property Damage Only CollisionAngles80032018/12/30 00:00:00+0012/30/2018 3:25:00 PMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN10Entering at angle00N
1946651-122.30421747.6695372195383084803097603642620MatchedIntersection26005.0NE PARK RD AND NE RAVENNA WB BVNaN1Property Damage Only CollisionAngles20022018/12/05 00:00:00+0012/5/2018 1:00:00 PMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN10Entering at angle00N
1946662-122.34456947.694547219539309170310450E879712MatchedBlockNaNAURORA AVE N BETWEEN N 90TH ST AND N 91ST STNaN2Injury CollisionAngles20022019/01/04 00:00:00+001/4/2019 1:46:00 PMMid-Block (not related to intersection)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearWetDaylightNaNNaNNaN10Entering at angle00N
1946671-122.36167247.5567222195413078043090843745813MatchedBlockNaNPUGET BLVD SW BETWEEN SW HUDSON ST AND DEAD END 1NaN1Property Damage Only CollisionOther10012018/11/28 00:00:00+0011/28/2018 9:34:00 PMMid-Block (not related to intersection)28MOTOR VEHICLE RAN OFF ROAD - HIT FIXED OBJECTNaNYRainingWetDark - Street Lights OnNaNNaNNaN50Fixed object00N
1946682-122.29082647.565408219543309534310814E871089MatchedBlockNaN34TH AVE S BETWEEN S DAKOTA ST AND S GENESEE STNaN2Injury CollisionHead On30022018/11/12 00:00:00+0011/12/2018 8:12:00 AMMid-Block (not related to intersection)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN24From opposite direction - both moving - head-on00N
1946691-122.34452647.690924219544309085310365E876731MatchedBlockNaNAURORA AVE N BETWEEN N 85TH ST AND N 86TH STNaN1Property Damage Only CollisionRear Ended20022018/12/18 00:00:00+0012/18/2018 9:14:00 AMMid-Block (not related to intersection)14MOTOR VEHICLE STRUCK MOTOR VEHICLE, REAR ENDYNRainingWetDaylightNaNNaNNaN13From same direction - both going straight - both moving - rear-end00N
1946702-122.30668947.6830472195453112803126403809984MatchedIntersection24760.020TH AVE NE AND NE 75TH STNaN2Injury CollisionLeft Turn30022019/01/19 00:00:00+001/19/2019 9:25:00 AMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN28From opposite direction - one left turn - one straight00N
1946712-122.35531747.6787342195463095143107943810083MatchedIntersection24349.0GREENWOOD AVE N AND N 68TH STNaN2Injury CollisionCycles20112019/01/15 00:00:00+001/15/2019 4:48:00 PMAt Intersection (intersection related)51PEDALCYCLIST STRUCK MOTOR VEHICLE FRONT END AT ANGLENaNNClearDryDuskNaNNaNNaN5Vehicle Strikes Pedalcyclist43080N
1946721-122.28936047.611017219547308220309500E868008MatchedBlockNaN34TH AVE BETWEEN E MARION ST AND E SPRING STNaN1Property Damage Only CollisionRear Ended20022018/11/30 00:00:00+0011/30/2018 3:45:00 PMMid-Block (not related to intersection)14MOTOR VEHICLE STRUCK MOTOR VEHICLE, REAR ENDNaNNClearWetDaylightNaNNaNNaN14From same direction - both going straight - one stopped - rear-end00N